Investigation of the Best AP Method for Predicting Compressive Strength in RAC

The use of recycled aggregate concrete (RAC) in the construction industry can contribute to environmental protection by reducing the consumption of natural resources. The quality of RAC is crucial for the durability and safety of structures. Poor quality RAC can lead to structural weaknesses and pose a safety risk. For this reason, comprehensive testing of RAC quality is required prior to construction. There are several parameters that indicate the structural strength of RAC. One of the most important of these parameters is the compressive strength \((f'RAC)\) . An adequate compressive strength of RAC allows the structures to withstand the expected loads. Various automatic programming methods are used to predict \(f'RAC\) . As far as we know, one of these methods, Immune Plasma Programming (IPP) and its versions, was used for the first time for reliable prediction of \(f'RAC\) values. The success of the methods was compared with that of Artificial Bee Colony Programming (ABCP). To evaluate the results, the best and mean values of the algorithms and the complexity of the generated models were analyzed. The results show the importance of the new IPP versions and a potentially lower complexity than the standard IPP.